Spotlight Papers (with Posters)
Schema Independent Relational Learning
Jose Picado, Arash Termehchy and Alan Fern
Jose Picado, Arash Termehchy and Alan Fern
Scalable Training and Serving of Personalized Models
Daniel Crankshaw, Xin Wang, Joseph Gonzalez and Michael Franklin
Daniel Crankshaw, Xin Wang, Joseph Gonzalez and Michael Franklin
MALT: Scalable peer-to-peer learning over infiniBand
Asim Kadav, Erik Kruus and Hao Li
Asim Kadav, Erik Kruus and Hao Li
PD^2F: Running Parameter Server within a Distributed Dataflow Framework
Nan Zhu, Lei Rao and Xue Liu
Nan Zhu, Lei Rao and Xue Liu
How to Intelligently Distribute Training Data to Multiple Compute Nodes: Distributed Machine Learning via Submodular Partitioning
Kai Wei, Rishabh Iyer, Shengjie Wang, Wenruo Bai and Jeff Bilmes
Kai Wei, Rishabh Iyer, Shengjie Wang, Wenruo Bai and Jeff Bilmes
Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms
Yuchen Zhang and Michael Jordan
Yuchen Zhang and Michael Jordan
Supporting Fast Iteration in Model Building
Manasi Vartak, Pablo Ortiz, Kathryn Siegel, Harihar Subramanyam, Samuel Madden and Matei Zaharia
Manasi Vartak, Pablo Ortiz, Kathryn Siegel, Harihar Subramanyam, Samuel Madden and Matei Zaharia
Speeding Up Distributed Machine Learning using Codes
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos and Kannan Ramchandran
Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos and Kannan Ramchandran
Exponential Stochastic Cellular Automata for Massively Parallel Inference
Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola and Guy Steele
Manzil Zaheer, Michael Wick, Jean-Baptiste Tristan, Alex Smola and Guy Steele
Poster Papers
CuMF: scale matrix factorization using just ONE machine with GPUs
Wei Tan, Liangliang Cao and Liana Fong
Wei Tan, Liangliang Cao and Liana Fong
Sources of Variability in Large-scale Machine Learning Systems
Damien Lefortier, Anthony Truchet and Maarten de Rijke
Damien Lefortier, Anthony Truchet and Maarten de Rijke
Brook: An Easy and Efficient Framework for Distributed Machine Learning
Chao Ma, Yan Ni and Zhen Xiao
Chao Ma, Yan Ni and Zhen Xiao
Fast FPGA System for Training Nonlinear Support Vector Machines
Mudhar Bin Rabieah and Christos-Savvas Bouganis
Mudhar Bin Rabieah and Christos-Savvas Bouganis
dreaml: A Library for Dynamic Reactive Machine Learning
Eric Wong, Terrence Wong and J. Zico Kolter
Eric Wong, Terrence Wong and J. Zico Kolter
Chainer: a Next-Generation Open Source Framework for Deep Learning
Seiya Tokui, Kenta Oono, Shohei Hido and Justin Clayton
Seiya Tokui, Kenta Oono, Shohei Hido and Justin Clayton
FACTORBASE : SQL for Multi-Relational Model Learning
Zhensong Qian and Oliver Schulte
Zhensong Qian and Oliver Schulte
SparkNet: Training Deep Networks in Apache Spark
Philipp Moritz, Robert Nishihara and Michael Jordan
Philipp Moritz, Robert Nishihara and Michael Jordan
SpeeDO: Parallelizing Stochastic Gradient Descent for Deep Convolutional Neural Network
Zhongyang Zheng, Wenrui Jiang, Gang Wu and Edward Y. Chang
Zhongyang Zheng, Wenrui Jiang, Gang Wu and Edward Y. Chang
Sparkling Vector Machines
Tu Dinh Nguyen, Vu Nguyen, Trung Le and Dinh Phung
Tu Dinh Nguyen, Vu Nguyen, Trung Le and Dinh Phung
Asynchronous Distributed Data Parallelism for Machine Learning
Zheng Yan and Yunfeng Shao
Zheng Yan and Yunfeng Shao
Asynchronous Complex Analytics in a Distributed Dataflow Architecture
Joseph Gonzalez, Peter Bailis, Michael Jordan, Michael Franklin, Joseph Hellerstein, Ali Ghodsi and Ion Stoica
Joseph Gonzalez, Peter Bailis, Michael Jordan, Michael Franklin, Joseph Hellerstein, Ali Ghodsi and Ion Stoica
XGBoost: Reliable Large-scale Tree Boosting System
Tianqi Chen and Carlos Guestrin
Tianqi Chen and Carlos Guestrin
Interactive machine learning using BIDMach
Biye Jiang and John Canny
Biye Jiang and John Canny
MXNet: A Distributed Deep Learning Framework for Efficiency and Flexibility
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang and Zheng Zhang
Tianqi Chen, Mu Li, Yutian Li, Min Lin, Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang and Zheng Zhang
Towards Geo-Distributed Machine Learning
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino and Giovanni Matteo Fumarola
Ignacio Cano, Markus Weimer, Dhruv Mahajan, Carlo Curino and Giovanni Matteo Fumarola